High performance motion detection: some trends toward new embedded architectures for vision systems

Lacassagne, Lionel; Manzanera, Antoine; Denoulet, Julien; Merigot, A.

doi:10.1007/s11554-008-0096-7

Cited by 18 publications

(11 citation statements)

References 30 publications

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“…Methods based on Σ − ∆ (sigma-delta) motion detection filters [35]- [37] are popular for embedded processing [38], [39]. As in the case of analog-to-digital converters, a Σ − ∆ motion detection filter consists of a simple non-linear recursive approximation of the background image, which is based on comparison and on an elementary increment/decrement (usually −1, 0, and 1 are the only possible updating values).…”

Section: Review Of Background Subtraction Algorithmsmentioning

confidence: 99%

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Barnich

Droogenbroeck²

2011

IEEE Trans. on Image Process.

1,737

1,109

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Abstract-This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based on the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel.We describe our method in full details (including pseudocode and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques. An implementation of ViBe is available at http://www.motiondetection.org.

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Section: Review Of Background Subtraction Algorithmsmentioning

confidence: 99%

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Barnich

Droogenbroeck²

2011

IEEE Trans. on Image Process.

1,737

1,109

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“…The Sigma-Delta algorithm [10] is a motion detection algorithm used in image processing to discriminate moving objects from the background. Contrary to simple thresholded background substraction algorithms, Sigma-Delta uses a per-pixel variance estimation to filter out outliers due to lighting conditions or contrast variation inside the image.…”

Section: Sigma-delta Motion Detectionmentioning

confidence: 99%

“…The whole algorithm can be expressed by a series of additions, substractions and various boolean selections. As pointed by Lacassagne in [10], the Sigma-Delta algorithm is mainly limited by memory bandwidth and so no optimizations beside SIMDization is efficient as only point-to-point operations are issued. Table 5 details how BOOST.SIMD performs against scalar and handwritten versions of the algorithm; the benchmarks are realized with gray scale images.…”

Section: Sigma-delta Motion Detectionmentioning

confidence: 99%

Boost.SIMD

Esterie

Falcou

Gaunard³

et al. 2014

Proceedings of the 2014 Workshop on Programming Models for SIMD/Vector Processing

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SIMD extensions have been a feature of choice for processor manufacturers for a couple of decades. Designed to exploit data parallelism in applications at the instruction level, these extensions still require a high level of expertise or the use of potentially fragile compiler support or vendor-specific libraries. While a large fraction of their theoretical accelerations can be obtained using such tools, exploiting such hardware becomes tedious as soon as application portability across hardware is required. In this paper, we describe BOOST.SIMD, a C++ template library that simplifies the exploitation of SIMD hardware within a standard C++ programming model. BOOST.SIMD provides a portable way to vectorize computation on Altivec, SSE or AVX while providing a generic way to extend the set of supported functions and hardwares. We introduce a C++ standard compliant interface for the users which increases expressiveness by providing a high-level abstraction to handle SIMD operations, an extension-specific optimization pass and a set of SIMD aware standard compliant algorithms which allow to reuse classical C++ abstractions for SIMD computation. We assess BOOST.SIMD performance and applicability by providing an implementation of BLAS and image processing algorithms.

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“…Among other real-time optical flow-based object extraction, this one seems to be applicable in scenarios where the system has to deal with analysis in compressed domain in real time. There are also available fast algorithms for motion detection for decompressed domain like [19] or [20] that can work on embedded platforms, so having other than MPEG2 compression does not constitute a problem in this case. System architect however has to be aware that in the latter solutions there is a need to have real-time video decompression and enhancement module which is usually a hardware module (especially in embedded systems) [30] or [31].…”

Section: Introductionmentioning

confidence: 99%

Real-time field sports scene classification using colour and frequency space decompositions

Kapela

McGuinness

O’Connor

2014

J Real-Time Image Proc

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This paper presents a novel approach to recognize a scene presented in an image with specific application to scene classification in field sports video. We propose different variants of the algorithm ranging from bags of visual words to the simplified real-time implementation, that takes only the most important areas of similar colour into account. All the variants feature similar accuracy which is comparable to very well-known image indexing techniques like SIFT or HoGs. For the comparison purposes, we also developed a specific database which is now available online. The algorithm is suitable in scene recognition task thanks to changes in speed and robustness to the image resolution, thus, making it a good candidate in real-time video indexing systems. The procedure features high simplicity thanks to the fact that it is based on the very well-known Fourier transform.

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High performance motion detection: some trends toward new embedded architectures for vision systems

Cited by 18 publications

References 30 publications

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Boost.SIMD

Real-time field sports scene classification using colour and frequency space decompositions

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